Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data

no code implementations • 25 Mar 2024 • Xinting Liao, Weiming Liu, Chaochao Chen, Pengyang Zhou, Fengyuan Yu, Huabin Zhu, Binhui Yao, Tao Wang, Xiaolin Zheng, Yanchao Tan

The former indicates that representation collapse in local model will subsequently impact the global model and other local models.

Federated Learning Federated Unsupervised Learning

Joint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Data

no code implementations • 17 Aug 2023 • Xinting Liao, Chaochao Chen, Weiming Liu, Pengyang Zhou, Huabin Zhu, Shuheng Shen, Weiqiang Wang, Mengling Hu, Yanchao Tan, Xiaolin Zheng

In server, GNE reaches an agreement among inconsistent and discrepant model deviations from clients to server, which encourages the global model to update in the direction of global optimum without breaking down the clients optimization toward their local optimums.

Federated Learning

HyperFed: Hyperbolic Prototypes Exploration with Consistent Aggregation for Non-IID Data in Federated Learning

no code implementations • 26 Jul 2023 • Xinting Liao, Weiming Liu, Chaochao Chen, Pengyang Zhou, Huabin Zhu, Yanchao Tan, Jun Wang, Yue Qi

Firstly, HPTI in the server constructs uniformly distributed and fixed class prototypes, and shares them with clients to match class statistics, further guiding consistent feature representation for local clients.

Federated Learning